How will AI change landscapes designed in the open air?
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Artificial intelligence does not replace human marketing teams, but it does support employees in managing campaigns or in content marketing. In the future, it could increasingly displace a popular means of expression in branding: emotions. A trend report.
The announcement in March that Zalando had replaced 250 marketing employees with artificial intelligence went through the media landscape like an alarming wildfire. On closer inspection, however, the AI threat is put into perspective: Zalando has decided to restructure a number of processes, for example to localize marketing measures even more strongly and to distribute content production decentrally to the sales countries, which is accompanied by organic staff cuts. Of course, it was Zalando smart, as a technology leader, to adorn itself with AI innovation leadership in marketing. Ultimately, however, only part of the marketing will in fact be replaced by algorithms.
Even if the message has far less impact than initially thought, AI will change marketing profoundly. A recent study by McKinsey shows that, based on the various operational functions in companies, the greatest potential of AI lies in marketing, with potential for value creation of up to six trillion US dollars. Interestingly, this contrasts with a very small number of successful AI applications in marketing - especially in German companies.
Artificial intelligence in the sense of a completely autonomous, self-learning system is in fact not yet a practical reality. There are impressive examples such as Google's Alphago, a program for the Go board game that has repeatedly beaten professional Go players and thus shows the increased potential of AI. But these programs are still based on a fairly mechanistic approach that diligently “learns” from large amounts of data. In a certain way, there are still laboratory situations that have little to do with corporate reality. The analogy between neural networks and the human brain is also misleading. Not only that, in purely quantitative terms, we are miles away from the quantity structures and relationships of neurons in the human brain. To this day we do not know exactly how the human brain works, but we want to recreate it. And yet: Even if the AI approaches pretend to be quasi-intelligence, they can sustainably support and change marketing. From a business perspective, the dimensions of automation and augmentation ultimately count:
How well can AI automate marketing processes and how well can AI support or optimize marketing decisions?
Most AI applications, like the Zalando example, relate to the automation of marketing functions and processes. In this context, systems also make simple decisions independently. This is usually about the substitution of human activities by artificial intelligence in order to achieve cost and efficiency advantages. There are many automation applications that already have a high level of maturity and are used in practice. This includes, for example, marketing automation or real-time bidding.
In contrast to this, augmentation applications are particularly about the intelligent support and enrichment of complex and creative marketing tasks, which are currently still usually carried out by human actors. In this way, the AI can automatically analyze competitors, target groups and trends. Marketing managers can use these insights to develop or adapt their strategy. This enriches decision-making processes with important information. The actual decision is not automated, but remains the sovereignty of human actors. In the sense of augmentation, AI can also help marketers to manage the increasing complexity of channels and touchpoints. In this way, both the value contribution of a channel and the necessary interaction of the channels to optimize the conversion can be calculated. On the basis of extensive customer journey data, the optimal media budget allocation can also be determined over time: When should which euro be invested in which channel? The final media plan, including the organizational role allocation, is then - at least today - created and evaluated by people. Due to the greater complexity and creativity of these tasks, both the degree of maturity and the distribution are significantly less pronounced compared to the automation examples using AI.
In the automation of marketing processes, AI is already showing a high degree of maturity and practical use. (Graphic: Peter Gentsch)
However, there are also applications which, despite their high degree of maturity, are still used comparatively little in practice today. One area of application to which this phenomenon applies is the principle of lookalikes, which can be used to identify and profile target groups. Artificial intelligence can collect around 10,000 data points on companies or consumers on the web and identify and profile new target groups using so-called deep learning algorithms. In the B2C area, for example, this can be easily implemented with Facebook custom audiences.
AI is also increasingly playing a major role in content marketing. Algorithms for the semantic conceptualization of content help here. For example, the Word-to-Vec algorithm automatically maps the content in the form of vectors that abstractly formalize the actual content. This representation is much more powerful than the typical index over a piece of content. On this basis, similar or complementary content can be found automatically on an analytical level. So-called “Long-Short-Term-Memory-Recurrent-Neural-Networks” can also produce new content in the sense of predictive content by predicting the next closest words and sentences based on words, taking into account the temporal context. One example is the AI-supported creation of an issue of the British marketing magazine The Drum. Thousands of copies were printed of the edition, in which the AI selected images, adjusted texts and designed the pages. The AI was fed with data from the winners of the Golden Lion from the Cannes Lions International Festival of Creativity. So it wasn't just about creating the magazine, but at the same time creating an artificial intelligence that meets the taste of the lifestyle audience. On the basis of such procedures, so-called robot journalism is also becoming increasingly creative. Algorithms are able to search the web automatically for information, bring it together and create a readable piece of content from it. Data-based reports are already often created automatically in the areas of sports, weather or finance.
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